Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine

Rafiei, Mehdi; Niknam, Taher; Aghaei, Jamshid; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1109/tsg.2018.2807845

Cited by 151 publications

(57 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the evaluation and comparison of the predictive uncertainties of various ML models, only a very limited number of works exists, mostly with the focus on one specific model only. For example, in [68], confidence intervals for the Gaussian Process (GP) regression have been reported and, in [69], prediction intervals have been studied for the energy load forecasts using the generalized extreme learning machine.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

2019

View full text Add to dashboard Cite

Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

2019

View full text Add to dashboard Cite

show abstract

“…It also analyses the effects of the weather conditions and forecasts on the prediction of solar power generation. A hybrid method consisting of a generalised extreme learning machine, wavelet pre-processing and bootstrapping for improving the accuracy of load forecasting which considers the uncertainties of the forecasting model and noise data is discussed in [259]. In [260], four different deep-learning models for forecasting electricity prices are presented and it is concluded that they generally provide better accuracy than statistical models.…”

Section: Challenges Of Microgridsmentioning

confidence: 99%

Evolution of Microgrids with Converter-Interfaced Generations: Challenges and Opportunities

Hossain¹,

Pota²,

Hossain³

et al. 2018

Preprint

View full text Add to dashboard Cite

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are revised according to their specific roles to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

show abstract

“…With the incorporation of circular buffers, we present the potential of this novel DF-DBA approach that can achieve better results in terms of throughput, Packet delivery ratio (PDR) and End-to-End Delay. Demand forecasting models have also been in use for different fields, such as business inventory management system [27,28], water distribution networks [29], neural networks [30,31], tourist system [32], and patient data management system [33]. However, to the best of our knowledge, this method is used for the first time in XG-PON networks to provide an easy way for ONU to receive the bandwidth grants.…”

Section: Introductionmentioning

confidence: 99%

Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON

et al. 2019

View full text Add to dashboard Cite

In a typical 10G-Passive Optical Network (XG-PON), the propagation delay between the Optical Network Unit (ONU) and Optical Line Terminal (OLT) is about 0.3 ms. With a frame size of 125 μs, this amounts to three frames of data in the upstream and three frames of data in the downstream. Assuming no processing delays, the grants for any bandwidth requests reach the ONU after six frames in this request-grant cycle. Often, during this six-frame delay, the queue situation is changed drastically, as much, more data would arrive in the queue. As a result, the queued data that is delayed loses its significance due to its real-time nature. Unfortunately, almost all dynamic bandwidth allocation (DBA) algorithms follow this request-grant cycle and hence lacking in their performance. This paper introduces a novel approach for bandwidth allocation, called Demand Forecasting DBA (DF-DBA), which predicts ONU’s future demands by statistical modelling of the demand patterns and tends to fulfil the predicted demands just in time, which results in reduced delay. Simulation results indicate that the proposed technique out-performs previous DBAs, such as GigaPON access network (GIANT) and round robin (RR) employing the request-grant cycle in terms of Throughput and Packet delivery ratio (PDR). Circular buffers are introduced in statistical predictions, which produce the least delay for this novel DF-DBA. This paper, hence, opens up a new horizon of research in which researchers may come up with better statistical models to brew better and better results for Passive optical networks.

show abstract

Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine

Cited by 151 publications

References 25 publications

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

Evolution of Microgrids with Converter-Interfaced Generations: Challenges and Opportunities

Demand Forecasting DBA Algorithm for Reducing Packet Delay with Efficient Bandwidth Allocation in XG-PON

Contact Info

Product

Resources

About